Diffuse　optical　tomography　(DOT)　is　a　promising　noninvasive　imaging　modality　and　is　capable　of　providing　functional　characteristics　of　biological　tissue　by　quantifying　optical　parameters.　The　DOT　image　reconstruction　is　ill-posed　and　ill-conditioned,　due　to　the　highly　diffusive　nature　of　light　propagation　in　biological　tissues　and　limited　boundary　measurements.　The　widely　used　regularization　technique　for　DOT　image　reconstruction　is　Tikhonov　regularization,　which　tends　to　yield　oversmoothed　and　low-quality　images　containing　severe　artifacts.　It　is　necessary　to　accurately　choose　a　regularization　parameter　for　Tikhonov　regularization.　To　overcome　these　limitations,　we　develop　a　noniterative　reconstruction　method,　whereby　optical　properties　are　recovered　based　on　a　back-propagation　neural　network　(BPNN).　We　train　the　parameters　of　BPNN　before　DOT　image　reconstruction　based　on　a　set　of　training　data.　DOT　image　reconstruction　is　achieved　by　implementing　a　single　evaluation　of　the　trained　network.　To　demonstrate　the　performance　of　the　proposed　algorithm,　we　compare　with　the　conventional　Tikhonov　regularization-based　reconstruction　method.　The　experimental　results　demonstrate　that　image　quality　and　quantitative　accuracy　of　reconstructed　optical　properties　are　significantly　improved　with　the　proposed　algorithm.　(C)　The　Authors.　Published　by　SPIE　under　a　Creative　Commons　Attribution　3.0　Unported　License.